Referenced Items are not available for this document.
We give a formal definition of geometric fitting in a way that suits computer vision applications. We point out that the performance of geometric fitting should be evaluated in the limit of small noise rather than in the limit of a large number of data as recommended in the statistical literature. Taking the KCR lower bound as an optimality requirement and focusing on the linearized constraint case, we compare the accuracy of Kanatani's renormalization with maximum likelihood (ML) approaches including the FNS of Chojnacki et al. and the HEIV of Leedan and Meer. Our analysis reveals the existence of a method superior to all these.
Published in:
3-D Digital Imaging and Modeling, 2005. 3DIM 2005. Fifth International Conference on
Date of Conference: 13-16 June 2005
- Page(s):
- 2 - 13
- ISSN :
- 1550-6185
- Print ISBN:
- 0-7695-2327-7
- INSPEC Accession Number:
- 8588223
- Digital Object Identifier :
- 10.1109/3DIM.2005.49
- Product Type:
- Conference Publications
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